Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets
arxiv(2024)
摘要
Sign language recognition (SLR) has recently achieved a breakthrough in
performance thanks to deep neural networks trained on large annotated sign
datasets. Of the many different sign languages, these annotated datasets are
only available for a select few. Since acquiring gloss-level labels on sign
language videos is difficult, learning by transferring knowledge from existing
annotated sources is useful for recognition in under-resourced sign languages.
This study provides a publicly available cross-dataset transfer learning
benchmark from two existing public Turkish SLR datasets. We use a temporal
graph convolution-based sign language recognition approach to evaluate five
supervised transfer learning approaches and experiment with closed-set and
partial-set cross-dataset transfer learning. Experiments demonstrate that
improvement over finetuning based transfer learning is possible with
specialized supervised transfer learning methods.
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